Overview

Dataset statistics

Number of variables15
Number of observations1500
Missing cells220
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory175.9 KiB
Average record size in memory120.1 B

Variable types

Numeric13
Categorical2

Alerts

avg_monthly_spend is highly overall correlated with debt_to_incomeHigh correlation
debt_to_income is highly overall correlated with avg_monthly_spend and 1 other fieldsHigh correlation
late_payment_count is highly overall correlated with payment_delay_ratioHigh correlation
monthly_income is highly overall correlated with debt_to_incomeHigh correlation
payment_delay_ratio is highly overall correlated with late_payment_countHigh correlation
repayment_issue is highly imbalanced (50.8%)Imbalance
monthly_income has 111 (7.4%) missing valuesMissing
employment_type has 109 (7.3%) missing valuesMissing
avg_monthly_spend has unique valuesUnique
credit_limit has unique valuesUnique
region_risk_score has unique valuesUnique
marketing_score has unique valuesUnique
cash_withdraw_ratio has 80 (5.3%) zerosZeros
late_payment_count has 438 (29.2%) zerosZeros
payment_delay_ratio has 438 (29.2%) zerosZeros

Reproduction

Analysis started2025-12-30 09:02:24.095910
Analysis finished2025-12-30 09:02:39.719911
Duration15.62 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

monthly_income
Real number (ℝ)

High correlation  Missing 

Distinct1389
Distinct (%)100.0%
Missing111
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean24312.409
Minimum6023.9174
Maximum102856.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:39.775639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6023.9174
5-th percentile11940.711
Q117140.737
median22431.662
Q328832.905
95-th percentile44377.032
Maximum102856.76
Range96832.84
Interquartile range (IQR)11692.168

Descriptive statistics

Standard deviation10266.223
Coefficient of variation (CV)0.42226266
Kurtosis3.8432056
Mean24312.409
Median Absolute Deviation (MAD)5737.1363
Skewness1.4241627
Sum33769937
Variance1.0539533 × 108
MonotonicityNot monotonic
2025-12-30T12:32:39.867191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26867.84941
 
0.1%
17004.363881
 
0.1%
31883.804791
 
0.1%
27695.718481
 
0.1%
17527.535911
 
0.1%
28462.341581
 
0.1%
45210.046591
 
0.1%
19683.97011
 
0.1%
14755.217031
 
0.1%
16767.643071
 
0.1%
Other values (1379)1379
91.9%
(Missing)111
 
7.4%
ValueCountFrequency (%)
6023.9174391
0.1%
6915.3430241
0.1%
7489.4103081
0.1%
7628.237311
0.1%
7724.1105681
0.1%
7813.3030811
0.1%
7930.1842271
0.1%
8105.0103731
0.1%
8195.5139141
0.1%
8353.6033971
0.1%
ValueCountFrequency (%)
102856.75761
0.1%
75474.665561
0.1%
65386.275891
0.1%
63130.091831
0.1%
62359.621011
0.1%
61813.912231
0.1%
61657.11561
0.1%
61330.577531
0.1%
61284.345511
0.1%
60522.650981
0.1%

avg_monthly_spend
Real number (ℝ)

High correlation  Unique 

Distinct1500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14841.587
Minimum3433.1021
Maximum78182.957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:39.944889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3433.1021
5-th percentile6517.8947
Q110037.315
median13347.461
Q318066.469
95-th percentile27664.739
Maximum78182.957
Range74749.855
Interquartile range (IQR)8029.1543

Descriptive statistics

Standard deviation7043.8681
Coefficient of variation (CV)0.47460342
Kurtosis7.8114719
Mean14841.587
Median Absolute Deviation (MAD)3857.1916
Skewness1.8729649
Sum22262381
Variance49616078
MonotonicityNot monotonic
2025-12-30T12:32:40.021851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18963.331731
 
0.1%
11779.404741
 
0.1%
50367.051061
 
0.1%
9214.7916641
 
0.1%
8198.301381
 
0.1%
10142.537271
 
0.1%
11509.306271
 
0.1%
20654.388571
 
0.1%
17270.857951
 
0.1%
15568.782751
 
0.1%
Other values (1490)1490
99.3%
ValueCountFrequency (%)
3433.1020871
0.1%
3477.2214261
0.1%
3557.5419231
0.1%
3588.1509121
0.1%
3668.2940781
0.1%
3880.1256681
0.1%
3958.2032171
0.1%
4080.2499081
0.1%
4138.5624181
0.1%
4217.297731
0.1%
ValueCountFrequency (%)
78182.957491
0.1%
56212.723411
0.1%
54829.674951
0.1%
54147.297861
0.1%
51193.433521
0.1%
50367.051061
0.1%
46251.400221
0.1%
44885.278581
0.1%
43912.50921
0.1%
43449.598541
0.1%

credit_limit
Real number (ℝ)

Unique 

Distinct1500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28205.613
Minimum9389.325
Maximum83707.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:40.091424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9389.325
5-th percentile14362.831
Q120795.99
median26705.462
Q333334.066
95-th percentile47632.179
Maximum83707.39
Range74318.065
Interquartile range (IQR)12538.076

Descriptive statistics

Standard deviation10185.057
Coefficient of variation (CV)0.36110036
Kurtosis1.5938151
Mean28205.613
Median Absolute Deviation (MAD)6240.9601
Skewness0.98057075
Sum42308419
Variance1.0373538 × 108
MonotonicityNot monotonic
2025-12-30T12:32:40.174990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13797.840241
 
0.1%
24050.13211
 
0.1%
10440.658451
 
0.1%
33103.640691
 
0.1%
16673.894521
 
0.1%
31742.78141
 
0.1%
27073.371041
 
0.1%
26610.969021
 
0.1%
19886.119171
 
0.1%
30522.724841
 
0.1%
Other values (1490)1490
99.3%
ValueCountFrequency (%)
9389.3249671
0.1%
9649.8047261
0.1%
9725.9623831
0.1%
9905.8489831
0.1%
9959.9015821
0.1%
9984.0643981
0.1%
10440.658451
0.1%
10713.334421
0.1%
10817.300391
0.1%
11089.055061
0.1%
ValueCountFrequency (%)
83707.38961
0.1%
81082.291071
0.1%
73419.222661
0.1%
72050.969381
0.1%
64798.957421
0.1%
64203.72251
0.1%
61216.774581
0.1%
60597.823331
0.1%
60341.217371
0.1%
60250.946651
0.1%

num_transactions
Real number (ℝ)

Distinct35
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.986667
Minimum18
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:40.251274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q131
median35
Q339
95-th percentile46
Maximum52
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.8076652
Coefficient of variation (CV)0.16599653
Kurtosis-0.11366584
Mean34.986667
Median Absolute Deviation (MAD)4
Skewness0.196153
Sum52480
Variance33.728975
MonotonicityNot monotonic
2025-12-30T12:32:40.322372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
35116
 
7.7%
33106
 
7.1%
36103
 
6.9%
3499
 
6.6%
3893
 
6.2%
3189
 
5.9%
3788
 
5.9%
3288
 
5.9%
3080
 
5.3%
2967
 
4.5%
Other values (25)571
38.1%
ValueCountFrequency (%)
181
 
0.1%
191
 
0.1%
206
 
0.4%
211
 
0.1%
229
 
0.6%
238
 
0.5%
2417
 
1.1%
2520
1.3%
2629
1.9%
2746
3.1%
ValueCountFrequency (%)
522
 
0.1%
514
 
0.3%
505
 
0.3%
494
 
0.3%
4819
1.3%
4716
1.1%
4626
1.7%
4523
1.5%
4425
1.7%
4338
2.5%

account_tenure_months
Real number (ℝ)

Distinct117
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.935333
Minimum3
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:40.402194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q133
median61
Q388
95-th percentile113.05
Maximum119
Range116
Interquartile range (IQR)55

Descriptive statistics

Standard deviation33.313486
Coefficient of variation (CV)0.54670228
Kurtosis-1.1577419
Mean60.935333
Median Absolute Deviation (MAD)28
Skewness-0.005390274
Sum91403
Variance1109.7883
MonotonicityNot monotonic
2025-12-30T12:32:40.483771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8326
 
1.7%
5025
 
1.7%
5921
 
1.4%
1520
 
1.3%
9219
 
1.3%
8819
 
1.3%
9318
 
1.2%
2618
 
1.2%
4918
 
1.2%
6817
 
1.1%
Other values (107)1299
86.6%
ValueCountFrequency (%)
310
0.7%
413
0.9%
514
0.9%
614
0.9%
714
0.9%
815
1.0%
910
0.7%
1012
0.8%
1114
0.9%
1211
0.7%
ValueCountFrequency (%)
11916
1.1%
11810
0.7%
1178
0.5%
11614
0.9%
11514
0.9%
11413
0.9%
11310
0.7%
11216
1.1%
11116
1.1%
1108
0.5%

cash_withdraw_ratio
Real number (ℝ)

Zeros 

Distinct1421
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24784721
Minimum0
Maximum0.71133783
Zeros80
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:40.562159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.14466459
median0.24554354
Q30.34504999
95-th percentile0.48998373
Maximum0.71133783
Range0.71133783
Interquartile range (IQR)0.2003854

Descriptive statistics

Standard deviation0.14325287
Coefficient of variation (CV)0.57798864
Kurtosis-0.44621981
Mean0.24784721
Median Absolute Deviation (MAD)0.10023439
Skewness0.18599451
Sum371.77081
Variance0.020521384
MonotonicityNot monotonic
2025-12-30T12:32:40.642850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080
 
5.3%
0.23657598891
 
0.1%
0.29751311761
 
0.1%
0.1970158261
 
0.1%
0.23407182081
 
0.1%
0.18785434131
 
0.1%
0.29703008161
 
0.1%
0.31770246771
 
0.1%
0.23601452371
 
0.1%
0.3383565311
 
0.1%
Other values (1411)1411
94.1%
ValueCountFrequency (%)
080
5.3%
3.712153783 × 10-51
 
0.1%
0.00080818769021
 
0.1%
0.0031425572991
 
0.1%
0.0032888736251
 
0.1%
0.0036463126911
 
0.1%
0.0038672407421
 
0.1%
0.00388607031
 
0.1%
0.0049597488721
 
0.1%
0.0054502533171
 
0.1%
ValueCountFrequency (%)
0.71133782971
0.1%
0.7077566981
0.1%
0.68293712061
0.1%
0.66854374321
0.1%
0.66084911921
0.1%
0.64557972991
0.1%
0.63030636221
0.1%
0.6296870851
0.1%
0.62068705671
0.1%
0.60826702251
0.1%

late_payment_count
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2006667
Minimum0
Maximum7
Zeros438
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:40.707594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0972096
Coefficient of variation (CV)0.91383361
Kurtosis1.7864473
Mean1.2006667
Median Absolute Deviation (MAD)1
Skewness1.0865422
Sum1801
Variance1.2038688
MonotonicityNot monotonic
2025-12-30T12:32:40.762680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1562
37.5%
0438
29.2%
2333
22.2%
3120
 
8.0%
429
 
1.9%
512
 
0.8%
65
 
0.3%
71
 
0.1%
ValueCountFrequency (%)
0438
29.2%
1562
37.5%
2333
22.2%
3120
 
8.0%
429
 
1.9%
512
 
0.8%
65
 
0.3%
71
 
0.1%
ValueCountFrequency (%)
71
 
0.1%
65
 
0.3%
512
 
0.8%
429
 
1.9%
3120
 
8.0%
2333
22.2%
1562
37.5%
0438
29.2%

age
Real number (ℝ)

Distinct50
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.360667
Minimum20
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:40.832351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q131
median44
Q357
95-th percentile67
Maximum69
Range49
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.674495
Coefficient of variation (CV)0.3307997
Kurtosis-1.2345379
Mean44.360667
Median Absolute Deviation (MAD)13
Skewness0.013778836
Sum66541
Variance215.34081
MonotonicityNot monotonic
2025-12-30T12:32:40.937492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2945
 
3.0%
2541
 
2.7%
2238
 
2.5%
6738
 
2.5%
5537
 
2.5%
4436
 
2.4%
3636
 
2.4%
6635
 
2.3%
2735
 
2.3%
2835
 
2.3%
Other values (40)1124
74.9%
ValueCountFrequency (%)
2032
2.1%
2132
2.1%
2238
2.5%
2327
1.8%
2423
1.5%
2541
2.7%
2623
1.5%
2735
2.3%
2835
2.3%
2945
3.0%
ValueCountFrequency (%)
6930
2.0%
6829
1.9%
6738
2.5%
6635
2.3%
6532
2.1%
6430
2.0%
6329
1.9%
6217
1.1%
6135
2.3%
6035
2.3%

region_risk_score
Real number (ℝ)

Unique 

Distinct1500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28121407
Minimum0.0035843361
Maximum0.83337276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:41.016577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0035843361
5-th percentile0.065057556
Q10.15864474
median0.26183109
Q30.37598316
95-th percentile0.57940552
Maximum0.83337276
Range0.82978842
Interquartile range (IQR)0.21733842

Descriptive statistics

Standard deviation0.15536935
Coefficient of variation (CV)0.55249492
Kurtosis-0.042978716
Mean0.28121407
Median Absolute Deviation (MAD)0.10887275
Skewness0.61301408
Sum421.82111
Variance0.024139634
MonotonicityNot monotonic
2025-12-30T12:32:41.101975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10679589161
 
0.1%
0.18150243111
 
0.1%
0.44899840521
 
0.1%
0.20830407061
 
0.1%
0.16459880731
 
0.1%
0.60805605991
 
0.1%
0.2033102761
 
0.1%
0.36022247081
 
0.1%
0.18690228471
 
0.1%
0.35351980661
 
0.1%
Other values (1490)1490
99.3%
ValueCountFrequency (%)
0.0035843361311
0.1%
0.011391517911
0.1%
0.014886790181
0.1%
0.018559194991
0.1%
0.021636186231
0.1%
0.021686293921
0.1%
0.023036281931
0.1%
0.023244413041
0.1%
0.026169337791
0.1%
0.026233061471
0.1%
ValueCountFrequency (%)
0.83337275881
0.1%
0.80227453941
0.1%
0.78775977981
0.1%
0.78224381811
0.1%
0.76396760251
0.1%
0.74785844021
0.1%
0.73672173421
0.1%
0.72697303081
0.1%
0.71745347291
0.1%
0.71524392821
0.1%

marketing_score
Real number (ℝ)

Unique 

Distinct1500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.105322
Minimum-7.8456299
Maximum109.13497
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size11.8 KiB
2025-12-30T12:32:41.174232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.8456299
5-th percentile26.140249
Q139.994974
median49.888985
Q360.087796
95-th percentile75.661661
Maximum109.13497
Range116.9806
Interquartile range (IQR)20.092822

Descriptive statistics

Standard deviation14.907166
Coefficient of variation (CV)0.29751661
Kurtosis0.16625385
Mean50.105322
Median Absolute Deviation (MAD)10.037038
Skewness0.12444713
Sum75157.983
Variance222.22358
MonotonicityNot monotonic
2025-12-30T12:32:41.249069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.591420631
 
0.1%
15.661591041
 
0.1%
47.250247371
 
0.1%
54.487934331
 
0.1%
39.111686241
 
0.1%
63.022632191
 
0.1%
58.260189841
 
0.1%
52.643190581
 
0.1%
58.123743981
 
0.1%
60.692072071
 
0.1%
Other values (1490)1490
99.3%
ValueCountFrequency (%)
-7.8456299391
0.1%
5.359512891
0.1%
6.326751211
0.1%
8.8776585581
0.1%
12.110580771
0.1%
12.112524711
0.1%
12.645774371
0.1%
12.713076321
0.1%
13.563314941
0.1%
13.636026941
0.1%
ValueCountFrequency (%)
109.13496521
0.1%
94.957974381
0.1%
94.072821011
0.1%
93.954804281
0.1%
92.705143831
0.1%
92.594343721
0.1%
91.788678711
0.1%
91.748527131
0.1%
90.600319111
0.1%
90.082351141
0.1%

app_login_count
Real number (ℝ)

Distinct30
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.79
Minimum5
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:41.308010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q117
median20
Q323
95-th percentile27
Maximum37
Range32
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.3924286
Coefficient of variation (CV)0.22195192
Kurtosis0.27117781
Mean19.79
Median Absolute Deviation (MAD)3
Skewness0.25541669
Sum29685
Variance19.293429
MonotonicityNot monotonic
2025-12-30T12:32:41.369115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
19140
 
9.3%
20138
 
9.2%
21135
 
9.0%
18132
 
8.8%
17123
 
8.2%
22115
 
7.7%
16108
 
7.2%
2388
 
5.9%
2480
 
5.3%
2574
 
4.9%
Other values (20)367
24.5%
ValueCountFrequency (%)
51
 
0.1%
83
 
0.2%
95
 
0.3%
108
 
0.5%
1117
 
1.1%
1228
 
1.9%
1346
3.1%
1454
3.6%
1568
4.5%
16108
7.2%
ValueCountFrequency (%)
371
 
0.1%
351
 
0.1%
345
 
0.3%
331
 
0.1%
323
 
0.2%
317
 
0.5%
3013
0.9%
2916
1.1%
2823
1.5%
2732
2.1%

employment_type
Categorical

Missing 

Distinct3
Distinct (%)0.2%
Missing109
Missing (%)7.3%
Memory size11.8 KiB
salaried
777 
self_employed
429 
other
185 

Length

Max length13
Median length8
Mean length9.1430625
Min length5

Characters and Unicode

Total characters12718
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowself_employed
2nd rowother
3rd rowother
4th rowself_employed
5th rowsalaried

Common Values

ValueCountFrequency (%)
salaried777
51.8%
self_employed429
28.6%
other185
 
12.3%
(Missing)109
 
7.3%

Length

2025-12-30T12:32:41.435967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T12:32:41.478877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
salaried777
55.9%
self_employed429
30.8%
other185
 
13.3%

Most occurring characters

ValueCountFrequency (%)
e2249
17.7%
l1635
12.9%
a1554
12.2%
s1206
9.5%
d1206
9.5%
r962
7.6%
i777
 
6.1%
o614
 
4.8%
f429
 
3.4%
_429
 
3.4%
Other values (5)1657
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)12718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2249
17.7%
l1635
12.9%
a1554
12.2%
s1206
9.5%
d1206
9.5%
r962
7.6%
i777
 
6.1%
o614
 
4.8%
f429
 
3.4%
_429
 
3.4%
Other values (5)1657
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2249
17.7%
l1635
12.9%
a1554
12.2%
s1206
9.5%
d1206
9.5%
r962
7.6%
i777
 
6.1%
o614
 
4.8%
f429
 
3.4%
_429
 
3.4%
Other values (5)1657
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2249
17.7%
l1635
12.9%
a1554
12.2%
s1206
9.5%
d1206
9.5%
r962
7.6%
i777
 
6.1%
o614
 
4.8%
f429
 
3.4%
_429
 
3.4%
Other values (5)1657
13.0%

debt_to_income
Real number (ℝ)

High correlation 

Distinct1468
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69892475
Minimum0.083774151
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:41.548056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.083774151
5-th percentile0.23361939
Q10.41056505
median0.60434265
Q30.88109814
95-th percentile1.5450082
Maximum2
Range1.9162258
Interquartile range (IQR)0.47053309

Descriptive statistics

Standard deviation0.40634102
Coefficient of variation (CV)0.58138022
Kurtosis1.5134801
Mean0.69892475
Median Absolute Deviation (MAD)0.22788628
Skewness1.2737035
Sum1048.3871
Variance0.16511303
MonotonicityNot monotonic
2025-12-30T12:32:41.622767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
233
 
2.2%
0.70580013471
 
0.1%
1.0877800291
 
0.1%
1.59841931
 
0.1%
0.3574252991
 
0.1%
0.2815044391
 
0.1%
0.5964667271
 
0.1%
0.51020589431
 
0.1%
0.64780187621
 
0.1%
0.44799808291
 
0.1%
Other values (1458)1458
97.2%
ValueCountFrequency (%)
0.083774150691
0.1%
0.08702511081
0.1%
0.10915525151
0.1%
0.1105745071
0.1%
0.11842583731
0.1%
0.12289584571
0.1%
0.12354955171
0.1%
0.12486067111
0.1%
0.1250117841
0.1%
0.13045734181
0.1%
ValueCountFrequency (%)
233
2.2%
1.9880093531
 
0.1%
1.9739250151
 
0.1%
1.9698871781
 
0.1%
1.9541529421
 
0.1%
1.9440221651
 
0.1%
1.9368701371
 
0.1%
1.9226834351
 
0.1%
1.9132478921
 
0.1%
1.8952025021
 
0.1%

payment_delay_ratio
Real number (ℝ)

High correlation  Zeros 

Distinct108
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034366023
Minimum0
Maximum0.26086957
Zeros438
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-12-30T12:32:41.697562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.028571429
Q30.054054054
95-th percentile0.095238095
Maximum0.26086957
Range0.26086957
Interquartile range (IQR)0.054054054

Descriptive statistics

Standard deviation0.032899888
Coefficient of variation (CV)0.95733765
Kurtosis3.5676985
Mean0.034366023
Median Absolute Deviation (MAD)0.028571429
Skewness1.3932259
Sum51.549034
Variance0.0010824026
MonotonicityNot monotonic
2025-12-30T12:32:41.770418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0438
29.2%
0.0294117647145
 
3.0%
0.0277777777841
 
2.7%
0.0270270270341
 
2.7%
0.0312535
 
2.3%
0.0256410256434
 
2.3%
0.0322580645231
 
2.1%
0.0333333333330
 
2.0%
0.0263157894729
 
1.9%
0.02528
 
1.9%
Other values (98)748
49.9%
ValueCountFrequency (%)
0438
29.2%
0.018867924531
 
0.1%
0.019230769231
 
0.1%
0.019607843143
 
0.2%
0.021
 
0.1%
0.020408163276
 
0.4%
0.020833333337
 
0.5%
0.021276595749
 
0.6%
0.0217391304315
 
1.0%
0.0222222222214
 
0.9%
ValueCountFrequency (%)
0.26086956521
0.1%
0.21052631581
0.1%
0.20689655171
0.1%
0.19230769231
0.1%
0.18421052631
0.1%
0.17241379311
0.1%
0.16666666671
0.1%
0.16129032262
0.1%
0.15789473681
0.1%
0.14634146341
0.1%

repayment_issue
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
1
1339 
0
161 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11339
89.3%
0161
 
10.7%

Length

2025-12-30T12:32:41.840126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T12:32:41.881150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11339
89.3%
0161
 
10.7%

Most occurring characters

ValueCountFrequency (%)
11339
89.3%
0161
 
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11339
89.3%
0161
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11339
89.3%
0161
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11339
89.3%
0161
 
10.7%

Interactions

2025-12-30T12:32:38.280098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:24.755756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.730677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.730642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.227976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.153631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.080783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.076456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.111101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.233479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.272665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.287067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.411264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.360859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:24.851458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.794983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.795188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.283163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.230394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.145345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.151943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.175707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.291986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.358694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.377165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.472553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.433595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:24.929840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.880634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.880950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.353940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.307231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.233029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.230562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.254995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.364234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.455367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.470534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.544384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.713375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.018869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.953047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.957328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.417758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.390177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.299375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.342873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.556347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.439742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.530593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.564340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.611261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.769043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.088450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.037612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:28.515435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.488914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.455203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.380359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.436347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.628766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.531153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.605226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.672801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.677050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.856814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.154878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.113774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:28.597697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.552280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.526380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.460364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.503383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.695266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.609571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.678228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.808689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.740097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.927262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.226158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.187514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:28.707087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.613749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.599749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.541719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.568771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.766109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.681050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.760388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.933798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.817994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:39.001558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.291574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.257913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:28.787887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.703585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.673253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.623301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.661439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.834821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.752290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.830658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.018981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.881973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:39.060443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.354237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.320301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:28.867564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.779958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.748824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.701669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.764849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.891761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.870536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.892294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.096264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.938516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:39.117862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.418410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.399770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:28.944809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.867337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.806829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.771083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.834845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.953272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.948956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.966440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.155988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.007405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:39.177904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.487073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.479849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.015746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.935002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.873827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.837760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.899334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.025422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.053861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.042890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.214954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.068675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:39.243041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.551401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.576502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.085317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.011164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.945779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.918377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.974381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.088564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.116115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.122910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.277230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.132612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:39.323133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:25.646492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:26.661220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:29.162274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:30.079984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:31.018827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:32.005779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:33.049653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:34.157173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:35.202440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:36.207175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:37.345243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T12:32:38.203016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-30T12:32:41.932627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
account_tenure_monthsageapp_login_countavg_monthly_spendcash_withdraw_ratiocredit_limitdebt_to_incomeemployment_typelate_payment_countmarketing_scoremonthly_incomenum_transactionspayment_delay_ratioregion_risk_scorerepayment_issue
account_tenure_months1.0000.005-0.0010.0070.0020.0160.0010.000-0.0390.0080.005-0.018-0.038-0.0130.050
age0.0051.0000.022-0.001-0.020-0.0050.0220.000-0.0010.034-0.029-0.0380.008-0.0120.054
app_login_count-0.0010.0221.0000.040-0.023-0.0230.0550.0770.0090.011-0.0400.0070.0140.0100.087
avg_monthly_spend0.007-0.0010.0401.0000.015-0.0090.7150.0370.0030.0080.033-0.0190.015-0.0400.221
cash_withdraw_ratio0.002-0.020-0.0230.0151.000-0.0290.0060.0000.028-0.0180.0010.0120.0240.0360.000
credit_limit0.016-0.005-0.023-0.009-0.0291.0000.0180.000-0.0360.029-0.0280.004-0.042-0.0310.000
debt_to_income0.0010.0220.0550.7150.0060.0181.0000.0000.0350.037-0.638-0.0210.043-0.0330.393
employment_type0.0000.0000.0770.0370.0000.0000.0001.0000.0660.0000.0000.0490.0650.0000.000
late_payment_count-0.039-0.0010.0090.0030.028-0.0360.0350.0661.000-0.033-0.045-0.0060.965-0.0020.099
marketing_score0.0080.0340.0110.008-0.0180.0290.0370.000-0.0331.000-0.0410.012-0.0320.0110.089
monthly_income0.005-0.029-0.0400.0330.001-0.028-0.6380.000-0.045-0.0411.0000.003-0.0390.0190.223
num_transactions-0.018-0.0380.007-0.0190.0120.004-0.0210.049-0.0060.0120.0031.000-0.216-0.0300.000
payment_delay_ratio-0.0380.0080.0140.0150.024-0.0420.0430.0650.965-0.032-0.039-0.2161.0000.0010.081
region_risk_score-0.013-0.0120.010-0.0400.036-0.031-0.0330.000-0.0020.0110.019-0.0300.0011.0000.000
repayment_issue0.0500.0540.0870.2210.0000.0000.3930.0000.0990.0890.2230.0000.0810.0001.000

Missing values

2025-12-30T12:32:39.454011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-30T12:32:39.547422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-30T12:32:39.663723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

monthly_incomeavg_monthly_spendcredit_limitnum_transactionsaccount_tenure_monthscash_withdraw_ratiolate_payment_countageregion_risk_scoremarketing_scoreapp_login_countemployment_typedebt_to_incomepayment_delay_ratiorepayment_issue
026867.84940018963.33172513797.840243311140.2365762600.10679659.59142124self_employed0.7058000.0625001
120841.35013510425.04567219907.76720236640.0284600200.08629063.39260816other0.5002100.0000001
228540.3859849244.74445923277.39654445500.3301580240.43879962.23780331other0.3239180.0000001
340506.26540813339.45540252088.04193944120.0000003600.29046253.79390820self_employed0.3293180.0666671
420057.10331612374.79374132688.989694341070.4145422390.23804756.23884012salaried0.6169780.0571431
520057.23503410894.87068216858.01433438380.4662291240.05869033.63869410salaried0.5431890.0256411
641426.87592818276.55466131892.07104529310.6028880550.09869438.73459818self_employed0.4411760.0000001
729940.69562520535.02705415653.40280426800.0138510480.19866763.43190320salaried0.6858570.0000001
818255.29119813901.93195239298.5094192660.0443652670.40175462.55814220salaried0.7615290.0740741
927365.10710325974.96676522813.46867441460.3539013530.15488560.85699130salaried0.9492000.0714291
monthly_incomeavg_monthly_spendcredit_limitnum_transactionsaccount_tenure_monthscash_withdraw_ratiolate_payment_countageregion_risk_scoremarketing_scoreapp_login_countemployment_typedebt_to_incomepayment_delay_ratiorepayment_issue
149017663.51941426433.23304330896.473955341020.2058361410.10810439.42007520salaried1.4964870.0285711
149161284.34551017517.45767719242.00378738830.2418991410.27982379.53288331NaN0.2858390.0256411
149217576.19942713799.89948721888.87331841840.3256870340.15013540.27465420NaN0.7851470.0000001
149323713.99173012142.97343722913.384101311180.3730721360.23158924.98324819self_employed0.5120590.0312500
149440816.5934958704.85933536309.90572828250.1879212640.41788762.90276222salaried0.2132680.0689661
149549140.41814313833.74579148391.622106311000.2375391470.41176448.27774420salaried0.2815150.0312501
149650241.73981715002.74769620296.21890638740.1361922410.32977245.45437319self_employed0.2986110.0512821
149735715.7301887640.41913339218.22537039150.2630861400.13526841.81669317other0.2139230.0250000
149833177.42687715527.67799812589.43065231250.3952762610.33708578.07571914self_employed0.4680190.0625001
149927917.54936812458.18889217712.63861233590.2651711360.15334055.31885420self_employed0.4462490.0294121